Brief Introduction to R and Descriptive Analysis of the Dataset

options(encoding = 'UTF-8')
#Loading all the necessary packages
if (!require("xts")) install.packages("caret")
if (!require("CASdatasets")) install.packages("CASdatasets", repos = "http://cas.uqam.ca/pub/", type="source")
if (!require("caret")) install.packages("caret")
if (!require("ggplot2")) install.packages("ggplot2")
if (!require("mgcv")) install.packages("mgcv")
if (!require("dplyr")) install.packages("dplyr")
if (!require("gridExtra")) install.packages("gridExtra")
if (!require("visreg")) install.packages("visreg")
if (!require("MASS")) install.packages("MASS")
if (!require("plotrix")) install.packages("plotrix")
if (!require("rgeos")) install.packages("rgeos", type="source")
if (!require("rgdal")) install.packages("rgdal", type="source")
if (!require("xtable")) install.packages("xtable")
if (!require("maptools")) install.packages("maptools")
if (!require("scales")) install.packages("scales")
if (!require("data.table")) install.packages
if (!require("broom")) install.packages("broom")
if (!require("stringi")) install.packages("stringi")



require("CASdatasets")
require("ggplot2")
require("mgcv")
require("caret")
require("gridExtra")
require("dplyr")
require("visreg")
require("MASS")
require("plotrix")
require("rgdal")
require("rgeos")
require("xtable")
require("maptools")
require("scales")
require("data.table")
require("broom")
require("stringi")

1 Introduction

## Loading the dataset
require("CASdatasets")
data("freMTPLfreq")

# 'Keep it simple' Old style : freMTPLfreq = subset(freMTPLfreq,
# Exposure<=1 & Exposure >= 0 & CarAge<=25) With tidyverse (dplyr in fact)
# See cheatsheet
# https://github.com/rstudio/cheatsheets/blob/main/data-transformation.pdf
dataset = freMTPLfreq %>%
    filter(Exposure <= 1 & Exposure >= 0 & CarAge <= 25)

saveRDS(dataset, file = "../dataset.Rds")

A good idea is to check whether the dataset has been loaded correctly. To do this, the following tools can be used:

• head allows to visualize the first 6 lines of the dataset.

head(dataset)

##   PolicyID ClaimNb Exposure Power CarAge DriverAge
## 1        1       0     0.09     g      0        46
## 2        2       0     0.84     g      0        46
## 3        3       0     0.52     f      2        38
## 4        4       0     0.45     f      2        38
## 5        5       0     0.15     g      0        41
## 6        6       0     0.75     g      0        41
##                                Brand     Gas             Region Density
## 1 Japanese (except Nissan) or Korean  Diesel          Aquitaine      76
## 2 Japanese (except Nissan) or Korean  Diesel          Aquitaine      76
## 3 Japanese (except Nissan) or Korean Regular Nord-Pas-de-Calais    3003
## 4 Japanese (except Nissan) or Korean Regular Nord-Pas-de-Calais    3003
## 5 Japanese (except Nissan) or Korean  Diesel   Pays-de-la-Loire      60
## 6 Japanese (except Nissan) or Korean  Diesel   Pays-de-la-Loire      60

• str allows to see the format of the different variables. We will typically distinguish numerical variables (real numbers or integers) and factors (categorical data).

str(dataset)

## 'data.frame':    410864 obs. of  10 variables:
##  $ PolicyID : Factor w/ 413169 levels "1","2","3","4",..: 1 2 3 4 5 6 7 8 9 10 ...
##  $ ClaimNb  : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ Exposure : num  0.09 0.84 0.52 0.45 0.15 0.75 0.81 0.05 0.76 0.34 ...
##  $ Power    : Factor w/ 12 levels "d","e","f","g",..: 4 4 3 3 4 4 1 1 1 6 ...
##  $ CarAge   : int  0 0 2 2 0 0 1 0 9 0 ...
##  $ DriverAge: int  46 46 38 38 41 41 27 27 23 44 ...
##  $ Brand    : Factor w/ 7 levels "Fiat","Japanese (except Nissan) or Korean",..: 2 2 2 2 2 2 2 2 1 2 ...
##  $ Gas      : Factor w/ 2 levels "Diesel","Regular": 1 1 2 2 1 1 2 2 2 2 ...
##  $ Region   : Factor w/ 10 levels "Aquitaine","Basse-Normandie",..: 1 1 8 8 9 9 1 1 8 6 ...
##  $ Density  : int  76 76 3003 3003 60 60 695 695 7887 27000 ...

• summary allows to compute for each variable some summary statistics.

summary(dataset)

##     PolicyID         ClaimNb           Exposure            Power      
##  1      :     1   Min.   :0.00000   Min.   :0.002732   f      :95432  
##  2      :     1   1st Qu.:0.00000   1st Qu.:0.200000   g      :90663  
##  3      :     1   Median :0.00000   Median :0.530000   e      :76784  
##  4      :     1   Mean   :0.03925   Mean   :0.559997   d      :67660  
##  5      :     1   3rd Qu.:0.00000   3rd Qu.:1.000000   h      :26558  
##  6      :     1   Max.   :4.00000   Max.   :1.000000   j      :17978  
##  (Other):410858                                        (Other):35789  
##      CarAge         DriverAge   
##  Min.   : 0.000   Min.   :18.0  
##  1st Qu.: 3.000   1st Qu.:34.0  
##  Median : 7.000   Median :44.0  
##  Mean   : 7.413   Mean   :45.3  
##  3rd Qu.:12.000   3rd Qu.:54.0  
##  Max.   :25.000   Max.   :99.0  
##                                 
##                                 Brand             Gas        
##  Fiat                              : 16653   Diesel :205299  
##  Japanese (except Nissan) or Korean: 79031   Regular:205565  
##  Mercedes, Chrysler or BMW         : 19087                   
##  Opel, General Motors or Ford      : 37287                   
##  other                             :  9738                   
##  Renault, Nissan or Citroen        :216684                   
##  Volkswagen, Audi, Skoda or Seat   : 32384                   
##                 Region          Density     
##  Centre            :159426   Min.   :    2  
##  Ile-de-France     : 69576   1st Qu.:   67  
##  Bretagne          : 41986   Median :  288  
##  Pays-de-la-Loire  : 38541   Mean   : 1987  
##  Aquitaine         : 31211   3rd Qu.: 1414  
##  Nord-Pas-de-Calais: 27111   Max.   :27000  
##  (Other)           : 43013

If one needs some help on a function, typing a question mark and the name of the function in the console opens the help file of the function. For instance,

?head

2 Descriptive Analysis of the portfolio

We will now have a descriptive analysis of the portfolio. The different variables available are PolicyID, ClaimNb, Exposure, Power, CarAge, DriverAge, Brand, Gas, Region, Density.

2.1 PolicyID

The variable PolicyID related to a unique identifier of the policy. We can check that every policy appears only once in the dataset

length(unique(dataset$PolicyID)) == nrow(dataset)

## [1] TRUE

Another possibility is to check the frequency of each PolicyID using the function table. The result is a table that shows for each PolicyID how many lines are in the dataset. We can then use a second time the function table in this result to show the frequency. We expect to have only ones (with possibily zeros), meaning each PolicyID has a unique line.

table(table(dataset$PolicyID))

## 
##      0      1 
##   2305 410864

To what corresponds the 0 ? It appears that in this dataset the variable PolicyID is a factor. A factor variable has different levels. It appears that some PolicyID may be missing here (removed from the dataset ?). It is as if we had a 3-level categorical variable, for instance, color of a car, which takes three possible values: red, blue, gray, but in our dataset, we would only have red and blue cars. Gray would still be a level, but with no observation (i.e. no row) corresponding to a gray car. To remove unused levels, we can rely on the function droplevels.

2.2 Exposure in month

The Exposure reveals the fraction of the year during which the policyholder is in the portfolio. We can compute the total exposure by summing the policyholders’ exposures. Here we find 230 082.6 years.

We can show the number of months of exposure on a table. The function cut allows to categorize (bin) a numerical variable. We can specify where to ‘break’ and give a name to each level using the labels argument. The output is a factor variable.

table(cut(dataset$Exposure, breaks = seq(from = 0, to = 1, by = 1/12), labels = 1:12))

## 
##      1      2      3      4      5      6      7      8      9     10 
##  62633  29216  33452  24213  19463  29565  18835  14438  21518  13653 
##     11     12 
##  12422 131456

Using the function prop.table, it is possible to represent this information in relative terms show the number of months of exposure on a table.

Exposures_prop = prop.table(table(cut(dataset$Exposure, breaks = seq(from = 0,
    to = 1, by = 1/12), labels = 1:12)))
round(Exposures_prop, 4)

## 
##      1      2      3      4      5      6      7      8      9     10 
## 0.1524 0.0711 0.0814 0.0589 0.0474 0.0720 0.0458 0.0351 0.0524 0.0332 
##     11     12 
## 0.0302 0.3200

Alternatively, we can use a barplot !

ggplot(dataset) + geom_bar(aes(x = cut(Exposure, breaks = seq(from = 0, to = 1,
    by = 1/12), labels = 1:12))) + scale_x_discrete(name = "Number of months") +
    scale_y_continuous(name = "Number of Policies", label = label_number()) +
    ggtitle("Exposure in months")

What if we also want to show the percentage on the bars ?

ggplot(dataset, aes(x = cut(Exposure, breaks = seq(from = 0, to = 1, by = 1/12),
    labels = 1:12), label = scales::percent(prop.table(stat(count)), accuracy = 0.1))) +
    geom_bar() + geom_text(stat = "count", vjust = -0.5, size = 3) + scale_x_discrete(name = "Number of months") +
    scale_y_continuous(name = "Number of Policies", label = label_number()) +
    ggtitle("Exposure in months")

Note that a barplot is used to plot factor variables (categorical variables). In our case, we categorized the variable Exposure using the function cut. If we do not want to categorize this variable, we should use a histogram. We can specify the number of bins (= 12) or the binwidth (= 1/12).

ggplot(dataset, aes(x=Exposure))+geom_histogram(binwidth =1/12, fill='gray', color='white') +
  scale_x_continuous(name = "Exposure in fraction of years", breaks=seq(0,1,1/12), labels = round(seq(0,1,1/12), 3))+
  scale_y_continuous(name = 'Number of Polices', labels = label_number()) + 
  ggtitle("Exposure in fraction of years")

If you are not familiar with ggplot, I could recommend this cheat-sheet: https://github.com/rstudio/cheatsheets/blob/main/data-visualization-2.1.pdf

2.3 Number of claim : ClaimNb

ggplot(dataset, aes(x = ClaimNb)) + geom_bar() + geom_label(stat = "count",
    aes(label = percent(prop.table(after_stat(count)), accuracy = 0.01)), vjust = 0.5) +
    scale_x_continuous(name = "Number of Claims") + scale_y_continuous(name = "Number of Polices",
    labels = label_number()) + ggtitle("Proportion of policies by number of claims")

We can compute the average claim frequency in this portfolio, taking into account the different exposures.

scales::percent(sum(dataset$ClaimNb)/sum(dataset$Exposure), accuracy = 0.01)

Here, we obtain 7.01%.

Let us now look at the other variables.

2.4 Power

The variable Power is a categorized variable, related to the power of the car. The levels of the variable are ordered categorically. We can see the different levels of a factor by using the function level in R:

levels(dataset$Power)

##  [1] "d" "e" "f" "g" "h" "i" "j" "k" "l" "m" "n" "o"

We can see the number of observations in each level of the variable, by using the function table.

table(dataset$Power)

## 
##     d     e     f     g     h     i     j     k     l     m     n     o 
## 67660 76784 95432 90663 26558 17398 17978  9270  4593  1758  1276  1494

Remember however, that in insurance, exposures may differ from one policyholder to another. Hence, the table above, does NOT measure the exposure in each level of the variable Power. We can use the functions group_by and summarise from package dplyr to give us the exposure in each level of the variable. Check out the cheatsheet https://github.com/rstudio/cheatsheets/blob/main/data-transformation.pdf

Power.summary = dataset %>%
    group_by(Power) %>%
    summarise(totalExposure = sum(Exposure), Number.Observations = length(Exposure))

We can show this on a plot as well:

ggplot(Power.summary, aes(x = Power, y = totalExposure, fill = Power, color = Power,
    label = scales::number(totalExposure))) + geom_bar(stat = "identity") +
    geom_text(stat = "identity", vjust = -0.5) + scale_y_continuous(name = "Exposure in years",
    labels = scales::number) + theme(legend.position = "none")

Let us now look at the observed claim frequency in each level

Power.summary = dataset %>%
    group_by(Power) %>%
    summarise(totalExposure = sum(Exposure), Number.Observations = length(Exposure),
        Number.Claims = sum(ClaimNb), Obs.Claim.Frequency = sum(ClaimNb)/sum(Exposure))
Power.summary

We can compute the ratio to the portfolio claim frequency.

portfolio.cf = sum(dataset$ClaimNb)/sum(dataset$Exposure)
# Can also be written as
portfolio.cf = with(dataset, sum(ClaimNb)/sum(Exposure))

ggplot(Power.summary, aes(x = Power, y = Obs.Claim.Frequency, color = Power,
    fill = Power, label = percent(Obs.Claim.Frequency, accuracy = 0.01))) +
    geom_bar(stat = "identity") + geom_hline(aes(yintercept = portfolio.cf),
    color = "black", size = 2, linetype = "dashed", alpha = 0.33) + geom_label(vjust = -0.21,
    fill = "white", alpha = 0.25) + annotate(geom = "text", x = "m", y = portfolio.cf,
    vjust = -0.5, label = paste("Average claim freq. of portfolio: ", percent(portfolio.cf,
        accuracy = 0.01)), color = "black") + scale_y_continuous(name = "Observed Claim Frequency",
    labels = percent_format(accuracy = 0.01)) + theme(legend.position = "none")

2.5 CarAge

The vehicle age, in years. This is the first continuous variable that we encounter (although it only takes discrete values).

ggplot(dataset, aes(x = CarAge)) + geom_bar() + scale_x_continuous(name = "Age of the Car",
    breaks = seq(0, 100, 5)) + scale_y_continuous(name = "Number of Polices",
    labels = label_number())

Alternatively, we can use a histogram, with a binwidth of 1.

ggplot(dataset, aes(x = CarAge)) + geom_histogram(binwidth = 1, color = "black",
    fill = "white") + scale_x_continuous(name = "Age of the Car", breaks = seq(0,
    100, 5)) + scale_y_continuous(name = "Number of Polices", labels = label_number())

Again, here, the exposures are not considered on the barplot/histogram. We can use ddply to correct this.

CarAge.summary = dataset %>%
    group_by(CarAge) %>%
    summarise(totalExposure = sum(Exposure), Number.Observations = length(Exposure))
CarAge.summary

Then, we can plot the data onto a barplot, as before.

ggplot(CarAge.summary, aes(x = CarAge, y = totalExposure, fill = factor(CarAge),
    color = factor(CarAge), label = label_number(accuracy = 1)(totalExposure))) +
    geom_bar(stat = "identity") + geom_text(stat = "identity", color = "black",
    hjust = 0.25, vjust = 0.5, angle = 45, check_overlap = TRUE) + scale_x_continuous(breaks = seq(0,
    100, 5)) + scale_y_continuous(name = "Exposure in years", labels = label_number()) +
    theme(legend.position = "none")

We can see a large difference, specially for new cars, which makes sense ! Indeed, let us look at the Exposure for recent vehicles, using a boxplot for instance.

ggplot(dataset[dataset$CarAge < 5, ], aes(x = CarAge, y = Exposure, group = CarAge)) +
    geom_boxplot() + ggtitle("Exposure of recent cars")

Let us now also compute the claim frequency by age of car,

CarAge.summary = dataset %>%
    group_by(CarAge) %>%
    summarise(totalExposure = sum(Exposure), Number.Observations = length(Exposure),
        Number.Claims = sum(ClaimNb), Obs.Claim.Freq = sum(ClaimNb)/sum(Exposure))
CarAge.summary

and plot it!

portfolio.cf = with(dataset, sum(ClaimNb)/sum(Exposure))

ggplot(CarAge.summary, aes(x = CarAge, y = Obs.Claim.Freq, label = percent(Obs.Claim.Freq,
    accuracy = 0.01))) + geom_bar(stat = "identity") + geom_hline(yintercept = portfolio.cf,
    color = "black", size = 2, linetype = "dashed", alpha = 0.33) + annotate(geom = "text",
    x = 20, y = portfolio.cf, vjust = -0.5, label = paste("Average claim freq. of portfolio: ",
        percent(portfolio.cf, accuracy = 0.01)), color = "black") + scale_x_continuous(name = "Age of the Car",
    breaks = seq(0, 100, 5)) + scale_y_continuous(name = "Observed Claim Frequency",
    labels = percent_format(accuracy = 0.01)) + theme(legend.position = "none")

2.6 DriverAge

Similarly to the Age of the Car, we can visualize the Age of the Drivers.

DriverAge.summary = dataset %>%
    group_by(DriverAge) %>%
    summarise(totalExposure = sum(Exposure), Number.Observations = length(Exposure),
        Number.Claims = sum(ClaimNb), Obs.Claim.Freq = sum(ClaimNb)/sum(Exposure))
head(DriverAge.summary, 9)

We can show the Exposures by Age of the Driver

ggplot(DriverAge.summary, aes(x = DriverAge, y = totalExposure)) + geom_bar(stat = "identity",
    width = 0.8) + scale_y_continuous(name = "Exposure in years", labels = label_number()) +
    scale_x_continuous(name = "Age of the Driver", breaks = seq(10, 150, 10))

and the observed claim frequency by Age.

ggplot(DriverAge.summary, aes(x = DriverAge, y = Obs.Claim.Freq)) + geom_line() +
    geom_point() + scale_y_continuous(name = "Observed Claim Frequency", labels = percent,
    breaks = seq(0, 0.5, 0.05)) + scale_x_continuous(name = "Age of the Driver",
    breaks = seq(10, 150, 10))

2.7 Brand

The variable Brand is a categorized variable, related to the brand of the car. We can see the different levels of a factor by using the function level in R:

levels(dataset$Brand)

## [1] "Fiat"                              
## [2] "Japanese (except Nissan) or Korean"
## [3] "Mercedes, Chrysler or BMW"         
## [4] "Opel, General Motors or Ford"      
## [5] "other"                             
## [6] "Renault, Nissan or Citroen"        
## [7] "Volkswagen, Audi, Skoda or Seat"

Brand.summary = dataset %>%
    group_by(Brand) %>%
    summarise(totalExposure = sum(Exposure), Number.Observations = length(Exposure),
        Number.Claims = sum(ClaimNb), Obs.Claim.Freq = sum(ClaimNb)/sum(Exposure))

Brand.summary

Brand	totalExposure	Number.Observations	Number.Claims	Obs.Claim.Freq
Fiat	9464.32	16653	714	0.07544
Japanese (except Nissan) or Korean	31228.97	79031	2078	0.06654
Mercedes, Chrysler or BMW	10392.17	19087	828	0.07968
Opel, General Motors or Ford	21733.56	37287	1731	0.07965
other	5676.08	9738	412	0.07259
Renault, Nissan or Citroen	133460.24	216684	8905	0.06672
Volkswagen, Audi, Skoda or Seat	18127.23	32384	1459	0.08049

ggplot(Brand.summary, aes(x = reorder(Brand, totalExposure), y = totalExposure,
    fill = Brand, label = label_number()(totalExposure))) + geom_bar(stat = "identity") +
    coord_flip() + guides(fill = "none") + scale_x_discrete(name = "") + scale_y_continuous("Exposure in years",
    labels = label_number(), expand = c(0.1, 0)) + geom_label()

Let us now look at the claim frequency by Brand of the car.

ggplot(Brand.summary, aes(x = reorder(Brand, Obs.Claim.Freq), y = Obs.Claim.Freq,
    fill = Brand, label = percent(Obs.Claim.Freq, accuracy = 0.1))) + geom_bar(stat = "identity") +
    geom_label(hjust = +1.2) + coord_flip() + guides(fill = "none") + ggtitle("Observed Claim Frequencies by Brand of the car") +
    scale_x_discrete(name = "Brand") + scale_y_continuous("Observed claim Frequency",
    labels = percent)

2.8 Gas

The variable Gas is a categorized variable, related to the fuel of the car. We can see the different levels of a factor by using the function level in R:

levels(dataset$Gas)

## [1] "Diesel"  "Regular"

Gas.summary = dataset %>%
    group_by(Gas) %>%
    summarise(totalExposure = sum(Exposure), Number.Observations = length(Exposure),
        Number.Claims = sum(ClaimNb), Obs.Claim.Freq = sum(ClaimNb)/sum(Exposure))
ggplot(Gas.summary, aes(x = Gas, y = totalExposure, fill = Gas, label = number(totalExposure))) +
    geom_bar(stat = "identity") + geom_label() + guides(fill = "none") + scale_x_discrete(name = "Fuel") +
    scale_y_continuous(name = "Total Exposure (in years)", labels = number)

There seems to be a similar amount of Diesel and Regular gas vehicles in the portfolio. It is generally expected that Diesel have a higher claim frequency. Does this also hold on our dataset ?

ggplot(Gas.summary, aes(x = Gas, y = Obs.Claim.Freq, fill = Gas, label = percent(Obs.Claim.Freq))) +
    geom_bar(stat = "identity") + geom_label() + guides(fill = "none") + scale_x_discrete(name = "Fuel") +
    scale_y_continuous("Observed claim Frequency", labels = percent)

2.9 Region

The variable Region is a categorized variable, related to the region of the place of residence. We can see the different levels of a factor by using the function level in R:

levels(dataset$Region)

##  [1] "Aquitaine"          "Basse-Normandie"    "Bretagne"          
##  [4] "Centre"             "Haute-Normandie"    "Ile-de-France"     
##  [7] "Limousin"           "Nord-Pas-de-Calais" "Pays-de-la-Loire"  
## [10] "Poitou-Charentes"

What are the Exposures in each region ? What are the observed claim frequencies ?

Region.summary = dataset %>%
    group_by(Region) %>%
    summarize(totalExposure = sum(Exposure), Number.Observations = length(Exposure),
        Number.Claims = sum(ClaimNb), Obs.Claim.Freq = sum(ClaimNb)/sum(Exposure))
Region.summary

Region	totalExposure	Number.Observations	Number.Claims	Obs.Claim.Freq
Aquitaine	14222.66	31211	1052	0.07397
Basse-Normandie	6621.74	10848	451	0.06811
Bretagne	27656.64	41986	1867	0.06751
Centre	101843.46	159426	6460	0.06343
Haute-Normandie	3147.22	8726	219	0.06959
Ile-de-France	30016.99	69576	2575	0.08578
Limousin	2376.00	4539	196	0.08249
Nord-Pas-de-Calais	11346.79	27111	939	0.08275
Pays-de-la-Loire	21791.75	38541	1569	0.07200
Poitou-Charentes	11059.29	18900	799	0.07225

We can plot a map with the observed claim frequencies and the total Exposure. We first need to obtain the shape files (which contain the borders of each administrative area.)

1. Download shapefile from http://www.diva-gis.org/gData
2. Extract all the files from the zip files, in a directory called shapefiles in your working directory

area <- rgdal::readOGR("shapefiles/FRA_adm1.shp", use_iconv = TRUE, encoding = "UTF-8")  # From http://www.diva-gis.org/gData

## OGR data source with driver: ESRI Shapefile 
## Source: "C:\Users\Florian\Documents\UCLouvain\Assurance Dommage\SummerSchool\1 - Introduction & Descriptive Analysis\shapefiles\FRA_adm1.shp", layer: "FRA_adm1"
## with 22 features
## It has 9 fields
## Integer64 fields read as strings:  ID_0 ID_1

# Note that the tidy function will remove the data.
area_tidy = tidy(area)  # package broom

# Plot an 'empty' map
ggplot(area_tidy, aes(x = long, y = lat, group = group)) + geom_polygon(color = "black",
    size = 0.1, fill = "lightgrey") + coord_equal() + theme_minimal()

We are now going to include our data into the map

# First we re-include the data (that disappeared with the tidy function)
area$id <- row.names(area)
area_tidy2 <- area_tidy %>%
    full_join(area@data, by = "id")

# Because of accents ...
area_tidy2$NAME_1 = stri_trans_general(str = area_tidy2$NAME_1, id = "Latin-ASCII")

# Which computed data do we want ?
data_to_add = Region.summary[, c("Region", "totalExposure", "Obs.Claim.Freq")]
# Merge it
area_tidy2 <- area_tidy2 %>%
    full_join(data_to_add, by = c(NAME_1 = "Region"))

# Very important: Do not forget to sort by 'order' variable.  area_tidy2 =
# area_tidy2[order(area_tidy2$order),] Easier with dplyr:
area_tidy2 = area_tidy2 %>%
    arrange(order)

ggplot(area_tidy2, aes(long, lat, group = group, fill = Obs.Claim.Freq)) + ggtitle("Observed Claim Frequencies") +
    geom_polygon(color = "black") + scale_fill_gradient(low = "green", high = "red",
    name = "Obs. Claim Freq.") + xlab("Longitude") + ylab("Latitude") + theme_void()

and the exposures (on a log-scale)…

ggplot(area_tidy2, aes(long, lat, group = group, fill = log(totalExposure))) +
    ggtitle("log Exposures in years") + geom_polygon(color = "black") + scale_fill_gradient(low = "green",
    high = "red", name = "log Exposure") + xlab("Longitude") + ylab("Latitude") +
    theme_void()

2.10 Density

The Density represents here the density of the population at the place of residence. Let us take a look at the densities in the dataset.

summary(dataset$Density)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##       2      67     288    1987    1414   27000

ggplot(dataset, aes(Density)) + geom_histogram(bins = 200)

Here, contrary to the age of the driver, or the age of the car, the density has lots of different values

length(unique(dataset$Density))

We can compute this by using the command above, and we get 1270.

Density.summary = dataset %>%
    group_by(Density) %>%
    summarise(totalExposure = sum(Exposure), Number.Observations = length(Exposure),
        Number.Claims = sum(ClaimNb), Obs.Claim.Freq = sum(ClaimNb)/sum(Exposure))
head(Density.summary)

Density	totalExposure	Number.Observations	Number.Claims	Obs.Claim.Freq
2	14.07	18	1	0.07107
3	107.92	152	4	0.03707
4	39.10	65	1	0.02558
5	119.65	217	12	0.10029
6	248.92	399	11	0.04419
7	378.44	580	16	0.04228

We can plot the observed claim frequencies…

ggplot(Density.summary, aes(x = Density, y = Obs.Claim.Freq)) + geom_point()

… but realize it is impossible to see a trend. One way out is to categorize the variable. We will see later (GAM) that it is possible to estimate a smooth function, which avoid the arbitrary categorization.

We can categorize the variable using the function cut.

dataset$DensityCAT = cut(dataset$Density, breaks = quantile(dataset$Density,
    probs = seq(from = 0, to = 1, by = 0.1)), include.lowest = TRUE)
table(dataset$DensityCAT)

## 
##              [2,28]             (28,51]             (51,91] 
##               41494               41173               41330 
##            (91,159]           (159,288]           (288,562] 
##               40432               41028               41408 
##      (562,1.16e+03] (1.16e+03,2.41e+03] (2.41e+03,4.35e+03] 
##               40889               41171               42408 
##  (4.35e+03,2.7e+04] 
##               39531

levels(dataset$DensityCAT) <- LETTERS[1:10]

Then, we can apply the same strategy as above.

Density.summary = dataset %>%
    group_by(DensityCAT) %>%
    summarise(totalExposure = sum(Exposure), Number.Observations = length(Exposure),
        Number.Claims = sum(ClaimNb), Obs.Claim.Freq = sum(ClaimNb)/sum(Exposure))
Density.summary

DensityCAT	totalExposure	Number.Observations	Number.Claims	Obs.Claim.Freq
A	26896.70	41494	1352	0.05027
B	25866.44	41173	1471	0.05687
C	25358.47	41330	1543	0.06085
D	24275.29	40432	1572	0.06476
E	24299.85	41028	1584	0.06519
F	24065.32	41408	1671	0.06944
G	22180.06	40889	1799	0.08111
H	21230.08	41171	1799	0.08474
I	19281.92	42408	1838	0.09532
J	16628.45	39531	1498	0.09009

ggplot(Density.summary, aes(x = DensityCAT, y = Obs.Claim.Freq, fill = DensityCAT,
    label = percent(Obs.Claim.Freq))) + geom_bar(stat = "identity") + geom_label() +
    guides(fill = "none") + scale_x_discrete(name = "Density") + scale_y_continuous("Observed claim Frequency",
    labels = percent)

3 Interactions

We can of course also dive into some interactions. For instance, we could analyse the effect of the car Age combined with the Fuel (Gas).

3.1 Fuel and Car Age

CarAge.Fuel.summary = dataset %>% group_by(CarAge, Gas) %>% 
                      summarise(totalExposure = sum(Exposure),
                                Number.Observations = length(Exposure),
                                Number.Claims = sum(ClaimNb),
                                Obs.Claim.Freq = sum(ClaimNb)/sum(Exposure))

## `summarise()` has grouped output by 'CarAge'. You can override using the
## `.groups` argument.

ggplot(CarAge.Fuel.summary, aes(x=CarAge, 
                                y=Obs.Claim.Freq)) + 
  facet_wrap(~Gas)+
  geom_bar(stat="identity") + 
  scale_x_continuous(name = "Age of the Car", breaks=seq(0,100,5))+
  scale_y_continuous(name = "Observed Claim Frequency", labels = percent_format(accuracy = 0.01))+
  theme(legend.position = 'none')

3.2 Fuel and Driver Age

We will illustrate another way to show this kind of data, by overlapping both bars.

DriverAge.Fuel.summary = dataset %>%
    group_by(DriverAge, Gas) %>%
    summarize(Obs.Claim.Freq = sum(ClaimNb)/sum(Exposure))

## `summarise()` has grouped output by 'DriverAge'. You can override using
## the `.groups` argument.

ggplot(data = DriverAge.Fuel.summary, aes(x = DriverAge, y = Obs.Claim.Freq,
    fill = Gas, color = Gas, alpha = Gas)) + geom_bar(stat = "identity", position = "identity") +
    scale_x_continuous(name = "Age of the Driver", breaks = seq(0, 100, 5)) +
    scale_y_continuous(name = "Observed Claim Frequency", labels = label_percent()) +
    scale_colour_manual(values = c("lightblue4", "red")) + scale_fill_manual(values = c("lightblue",
    "pink")) + scale_alpha_manual(values = c(0.3, 0.8)) + theme_bw()

4 Useful Links

• https://github.com/rstudio/cheatsheets/blob/main/data-transformation.pdf
• https://github.com/rstudio/cheatsheets/blob/main/data-visualization-2.1.pdf